Biological Information and Intelligent Design: Meyer, Yarus, and the Direct Templating Hypothesis

Then it’s good that we don’t say that. We say that some organisms are more likely to leave offspring, and we define that quality as “more fit”.

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How does one identify those organisms who are more likely to leave offspring? Let us say that we can say that fat organisms are more likely to leave offspring (and to verify this statement), then we can say that fat organisms are fit.

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Usually, you identify traits that are more or less fit, by counting offspring or by looking for rapidly increasing allele frequencies. You can define the fitness of an individual organism by averaging over their traits, but that’s only a realistic exercise for artificial strains with minimal genetic variation.


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Exactly. There are objective things about a man that make him attractive – whether or not we’ve figured out what they are.

Did you see what you just did there? You went from “There must be objective features that make him attractive” to “I should be able to determine…” Those are not analogous statements at all. The analogous statement is, “There must be objective features that determine the fitness of a cheetah.” And there are, whether we understand them or not.

Now, in both cases, we understand some of the objective features, but not all of them. I can tell you with little doubt that a lame cheetah, or one with bad eyesight, or a bad digestive system, will be less fit; I can’t tell you whether a 2% faster cheetah will be less fit. I can tell you that I’m less attractive than Valentino; I can’t tell you whether Tom Cruise is less attractive than Valentino.

The rest of your argument founders on this basic confusion.

I will also note that there’s nothing at all unusual about distinguishing between a propensity for success and actual success; it’s part of how we interpret the world. As has already been noted, the superior army does not always win the battle. The most attractive man does not always attract the most women. The best team does not always win the World Cup. Sometimes VHS beats Betamax. The race is not to the swift nor the battle to the strong.


Your statement still has some issues that need unpacking. “Barring accidents, the most fit individual will be selected” is not a statement I can assign a meaning to in this framework. I would say that, including accidents, the most fit individual is more likely to reproduce. Success at reproduction is a random process, i.e. it is nothing but accidents; all fitness tells you is how likely an individual is to have more of the successful accidents. There’s no deterministic process that goes on in the absence of accidents.

As is happens, we’re typically more interested in the fate of fitter traits, rather than fitter individuals. We can say, for example, that a newly arisen trait (specifically an additive trait, i.e. neither dominant nor recessive) that gives a 1% fitness advantage has a 2% probability of spreading throughout the population. That’s an unusually large selective advantage, and you see that it’s still easily swamped by the noise of random reproductive success. That’s why viewing success as the normal result – the thing that happens barring accidents – is not likely to give you a good picture of the situation. Fitness is usually a modest bias in a stochastic process.

Even with this clarification, though, there is an additional, subtler complication. If we agree to define the fitter trait as the one that causes the individual to have more offspring on average, then natural selection is the idea that fitter traits will be more likely to increase in frequency than less fit traits (note that this is again a propensity), all other things being equal. The kicker is that sometimes all other things aren’t equal. It sometimes happens that the less fit variant also has the effect of increasing its own inclusion in gametes, so that it’s more likely to be passed on. (Cartoon example: sperm that have the variant kill sperm that don’t have it.) In this case, the less fit trait can still spread and take over the population. To properly handle this kind of situation, you have to treat natural selection as operating on multiple levels, i.e. both the genetic and the organismal level. So a single simple statement about natural selection isn’t adequate.

It’s useful in a variety of contexts. Often it’s estimated retrospectively. It is of some biological interest, for example, that lactose tolerance provided a very large fitness advantage. It’s surprising and puzzling that this simple trait should be such an outlier in selection strength, and scientists like surprises and puzzles.

For another kind of use, consider this paper, which uses a rough guess at the fitness cost of a disease to give practical guidance on how to design genetic association studies. Such association studies are a big deal in the world of human disease genetics at present.


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Hi Eddie -

You seem to be advocating that scientists stop using Bayesian methods. Given the remarkable success of Bayesian methods across the sciences, I don’t think your campaign is going to succeed. I give it a 1% chance of success.

But of course, your campaign could in fact go viral, knock out the competition, and sweep the field of philosophy of science, even if it’s not very “fit” in this competition between methods…

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Interesting how the widespread use of contraceptives has made that statement problematic in humans.

Bayesian methods are precisely (or I suppose one should really say “approximately”!) as good as the scientific model into which they’re plugged. And are still vulnerable to the number of variables and how well they can be defined.


I haven’t commented here for a while, but I have caught up on most of the comments, and I think I see a pattern that might be worth mentioning.

The argument seems to boil down to whether or not its possible to predict the results of natural selection, without a priori (or a posteriori for that matter) knowledge of some quantitative estimate of fitness (which I think Steve has pretty well defined).

The answer is no, for the reason that Steve has given, it is extremely difficult to make accurate predictions in very complex scenarios. Since all of biology sets the standard for complexity among the sciences, it isn’t surprising that the central theory of biology, evolution by natural selection, would suffer from this problem. But biology is not alone. The three body problem in physics suffers similarly, and the solution of the Schrodinger equation for elements much larger than hydrogen does also.

This lack of predictability is not a hallmark of a bad science. The old idea that science always makes testable predictions has been modified greatly over the past century, ever since the uncertainty principle showed that in some cases, non-predictability is the rule. Chaos theory treats the idea of non-predictability in mathematical terms, and demonstrates its application to a large number of deterministic processes throughout the physical and social sciences.

So what all of this means is that yes, natural selection is a highly complex phenomenon whose outcomes cannot be predicted on empirical grounds based on quantitative assessments of fitness measures. But, again as Steve has shown, this doesn’t mean that such measures don’t exist, both absolute and relative fitness can be measured quantitatively and used in population genetics and Hardy Weinberg calculations to make useful predictions about how evolution works.

Here is an illustration from my own work. My group discovered a new allele of a human metabolic gene. We assessed its frequency in several populations. We found that it followed Hardy Weinberg equilibrium, which implied that it was not undergoing natural selection. We therefore predicted that it might not have any effect on fitness. After some further study, we found that indeed, as predicted, this allele had no effect on the health of the population (because of the activity of the gene in question, we thought it might)… So, while we never actually measured the fitness of the allele, the genotype, or the phenotype of the people who had the allele, it was still possible to make predictions about its role in natural selection based on biological law.

In other words, Bayesian

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Eddie, take a deep breath. You are right that there is a confusion about words, and what is being discussed. I do not disagree in fact with anything that Steve has written, so the confusion is not between us. My example was about the effect of the allele on people’s health. I wanted to show that biologists are not totally in the woods when it comes to mathematical analysis of the effects of genetic changes.

We made a guess (based on the position of the SNP and the activity of the gene) that the new allele could possibly have a deleterious effect on the health of the carriers. If so, then we would have expected to see a loss of HW equilibrium due to negative selection (which is more common than positive selection, btw). We did not see that. By itself, this does not prove that the new allele is neutral, but it is consistent with that. And that “prediction” (used loosely) turned out to be correct.

Your comment about humans choosing (based on cultural factors) how many kids to have is quite right, and could overwhelm any strictly genetic factors. But strongly deleterious alleles, will still in many cases reduce survival (for example of newborns or children) and averaged over an entire population that will show up as a disequilibrium in allele frequencies as selection plays a role. I would suggest checking out the HW equilibrium and its relation to selection, online. Its a very simple equation, and very useful in population genetics.

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No, that’s not quite right, Eddie. We did quantify the relative fitness of the new allele. It was 1.0. In other words it had no selective effect, either positive or negative. That was the result of the HW calculation. If we had found dysequilibrium, we could have quantified the relative fitness as being something like 1.2 or 0.95 etc. But note that my example is not related to evolutionary outcomes except tangentially, because I wasnt working on evolution at the time, but on population genetics in disease. Steve, who does work directly in evolutionary biology, uses far more sophisticated models to determine the quantitative fitness of genetic and phenotypic changes. My point was solely to illustrate that there are many ways to come up with numerical estimates of fitness. Which can then be used for various purposes. Im sorry if this isnt clear, but feel free to ask more questions.

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